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\name{Shusen Liu}

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\address{
Address: 72 S Central Campus Drive, R4720, Salt Lake City, UT 84112 \ \ \  Email: shusenl@sci.utah.edu 
}

\begin{resume}

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\section{\textsc{Research Interest}}
My research interests lie primarily in high-dimensional data analysis \& visualization as well as multivariate volume visualization

\section{\textsc{Education}}

\textbf{University of Utah} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 2009 - Present \\
Ph.D. in Computing, Graphics Track  \ \ \ \  Advisor Dr. Valerio Pascucci (pascucci@sci.utah.edu)

\textbf{Huazhong University of Science and Technology} \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ 2005 - 2009 \\ 
BS. in Computer Science \ \ \ BE. in Biomedical Engineering

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\section{\textsc{Journal Publications}} 
\textbf{Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections.} \\
\underline{Shusen Liu}, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci.
\emph{Computer Graphics Forum (EuroVis15)}, 2015

\textbf{Distortion Guided Structure-Driven Interactive Data Exploration.}\\
\underline{Shusen Liu}, Bei Wang, Valerio Pascucci.
\emph{Computer Graphics Forum 33 (3), 101-110}, 2014. 

\textbf{CT based computerized identification and analysis of human airways: A review.}\\
Jiantao Pu, Suicheng Gu, \underline{Shusen Liu}, Shaocheng Zhu, David Wilson, Jill M. Siegfried, David Gur.
\emph{Med. Phys. 39, 2603 (2012), DOI:10.1118/1.4703901}, 2012. 

\textbf{Feature-Based Statistical Analysis of Combustion Simulation Data.}\\
Janine Bennett, Vaidyanathan K, \underline{Shusen Liu}, Ray Grout, Jackie Chen, Bremer Timo, Valerio Pascucci.
\emph{IEEE Transaction on Computer Graphics and Visualization}, 2011. 

\textbf{Fast blood flow visualization of high-resolution laser speckle imaging data using graphics processing unit.}\\
\underline{Shusen Liu}, Pengcheng Li, Qingming Luo.
\emph{Optics Express, Vol.16, Issue 19}, 2008. 

\section{\textsc{Peer-Reviewed Conference Publications}} 
\textbf{Visualizing High-Dimensional Data: Advances in the Past Decade.} \\
\underline{Shusen Liu}, Dan Maljovec, Bei Wang, Peer-Timo Bremer, Valerio Pascucci.
\emph{The Eurographics Conference on Visualizaiton (EuroVis15), State-of-The-Art Reports (STARS)}, 2015

\textbf{Multivariate Volume Visualization through Dynamic Projections.} \\
\underline{Shusen Liu}, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci.
\emph{IEEE Symposium on Large Data Analysis and Visualization (LDAV)}, 2014

\textbf{Analyzing Simulation-Based PRA Data Through Clustering: a BWR Station Blackout Case Study.}\\
Dan Maljovec, \underline{Shusen Liu}, Bei Wang, Valerio Pascucci, Peer-Timo Bremer, Diego Mandelli, Curtis Smith.
\emph{International Conference on Probabilistic Safety Assessment and Management (PSAM)}, 2014

\textbf{Gaussian Mixture Model Based Volume Rendering.}\\
\underline{Shusen Liu}, Joshua A. Levine, Peer-Timo Bremer, Valerio Pascucci.
\emph{IEEE Symposium on Large-Scale Data Analysis and Visualization (Best paper award)}, 2012. 

\newpage
\ \newline

%\section{\textsc{Submitted}} 
\section{\textsc{Other Publications}}
\textbf{Visual Exploration of High-Dimensional Data: Subspace Analysis through Dynamic Projections}\\
\underline{Shusen Liu}, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci.
\emph{SCI Technical Report, No. UUSCI-2014-003, SCI Institute, University of Utah}, 2014

\textbf{A Static Load Balancing Scheme for Parallel Volume Rendering on Multi-GPU Clusters} (Poster)\\
\underline{Shusen Liu}, Venkatram Vishwanath, Joseph Insley, Mark Hereld, Michael E. Papka, Valerio Pascucci.
\emph{IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)}, 2012

\textbf{Evaluating graph coloring on GPUs} (Poster)\\
Pascal Grosset, Peihong Zhu, \underline{Shusen Liu}, Suresh Venkatasubramanian, Mary W. Hall.
\emph{ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming}, 2011

%\href{http://scholar.google.com/citations?user=BFy2MWAAAAAJ&hl=en}{Google citation}

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\section{\textsc{Work Experience}}

\employer{\textbf{Scientific Computing and Imaging Institute, University of Utah}}
\title{Research Assistant}
\location{Salt Lake City, UT}
\dates{2010 - Present}
\begin{position}
Developing new volume rendering algorithms and high dimensional data visualization techniques.
\end{position}

\employer{\textbf{Lawrence Livermore National Laboratory}}
\title{Research Aide Intern}
\location{Livermore, CA}
\dates{Summer 2014, 2013}
\begin{position}
Developed high dimensional data visualization techniques.(2014) \\
Developed segmentation algorithm for industrial CT-scan (2013)
\end{position}

\employer{\textbf{Argonne National Laboratory}}
\title{Research Aide Intern}
\location{Lemont, IL}
\dates{Summer 2012, 2011}
\begin{position}
Optimized GPU cluster load balance for vl3 parallel volume renderer (2012).\\
Improved rendering quality and streaming speed of vl3 parallel volume renderer (2011).
\end{position}

\employer{\textbf{Wuhan National Laboratory for Optoelectronic
}}
\title{Research Assistant}
\location{Wuhan, China}
\dates{2007 - 2009}
\begin{position}
Developed GPU accelerated realtime laser speckle imaging system. (2007-2008)\\
%Developed GPU accelerated tools for biomedical photonics study. (2007-2008)
Developed volume rendering tool for the Visible Chinese Human project (2008-2009)
\end{position}


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%\section{\textsc{Selected Projects}}
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%\employer{\textbf{An Interactive Embedding Manipulation Framework}}
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%\dates{2013 - present}
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%\begin{position}
%Developing a framework allow structure preserving manipulation of dimension reduction results. The system will provide real-time feedback of both global objective function and local distorsion.  
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%\employer{\textbf{Graph based Transfer Function Design Interface}}
%\dates{2013 - present}
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%Used skeleton of high-dimensional space as a transfer function design interface for visualizing multi-variate volume dataset.
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%\employer{\textbf{Load Balancing Scheme for Parallel Volume Renderer}}
%\dates{Summer 2012}
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%Provided a data partition scheme that greatly improved load balancing on GPU cluster.
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%\employer{\textbf{Gaussian Mixture Model based Volume Visualization}}
%\dates{2011 - 2012}
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%Used Gaussian Mixture Model to capture the per-voxel distribution in climate ensemble. Provide rendering algorithm on GPU for volume represented as GMM.(Best Paper Award at LDAV)
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%\employer{\textbf{Giga-voxel GPU Volume Renderer}}
%\dates{2010 - 2011}
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%Developed prototype volume renderer allows interactive rendering of volume dataset that are several times larger than the GPU memory.
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%\employer{\textbf{GPU Accelerated Real-time Laser Speckle Imaging System}}
%\dates{2007 - 2008}
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%Used CUDA to accelerate the laser speckle imaging system
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\section{\textsc{Awards}}
2012 Large Data Analysis and Visualization(LDAV) Best Paper Award\\
2008 Excellent Student Research Award, Huazhong University of Science and Technology


\section{\textsc{Selected Courses}}
Interactive Computer Graphics,\  {\bf Project}: Image Space Global illumination: SSAO SSDO\\
Scientific Visualization,\  {\bf Project}: Voxel Fractal Visualization (CUDA ray tracer)\\
Character Animation, \ {\bf Project}: Mean Value Coordinate based Mesh Deformation\\
Computational Geometry, \ {\bf Project}: Voronoi Diagram guided Shatter Effects\\
Parallel Programming for GPU,\  {\bf Project}: GPU Graph Coloring (Poster Published)\\
Advanced Algorithm, \ {\bf Project}: Cache Oblivious Algorithms Survey\\

\section{\textsc{Qualifications}}

\emph{Programming Languages, APIs}: \Cplusplus, python, R, OpenGL, glsl, CUDA, Qt, Boost \\
\emph{Tools}: CMake, GNU toolchain, Visual Studio, Vim, Matlab, \LaTeX, Paraview, Maya


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% \section{\textsc{Activities}}
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% \employer{\textbf{Cornell Teaching Assistant (TA Excellence Award)}}
% \dates{Fall 2006}
% \begin{position}
% Teaching assistant for new course on social and information networks.
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% \employer{\textbf{Cornell Teaching Assistant (TA Excellence Award)}}
% \dates{Fall 2005 - Spring 2006}
% \begin{position}
% Taught two sections of CS 100M during the Fall and Spring semesters of the 2005-6 academic year.  Received award in recognition of performance.
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% \employer{\textbf{UIUC ACM Treasurer}}
% \dates{Fall 2004 - Spring 2005}
% \begin{position}
% Managed all financial responsibilities of local chapter of ACM.  Assisted the Chair in general management of ACM.
% \end{position}
% 
% \employer{\textbf{UIUC ACM SIGGRAPH Chair}}
% \dates{Spring 2004}
% \begin{position}
% Managed the local chapter of SIGGRAPH, organized projects and workshops/tutorials
% \end{position}



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%\section{\textsc{Course Work}}
%  \begin{tabular}{lllll}
%  Information Networks   & \ \ & Machine Learning    & \ \ & Theory of Computation \\ 
%  Computer Graphics      & \ \ & Machine Vision      & \ \ & Programming Languages \\
%  Software Engineering   & \ \ & Algorithms          & \ \ & Artificial Intelligence     \\
%  Operating Systems      & \ \ & Databases           & \ \ & Computer Architecture \\
%  Numerical Methods      & \ \ & Graph Theory        & \ \ & Differential Equations      \\
%  Probability Theory     & \ \ & Number Theory       & \ \ & Differential Geometry       \\
%  Advanced Calculus      & \ \ & Abstract Algebra    & \ \ & Advanced Combinatorics   \\
%  \end{tabular}


\newpage

\section{\textsc{Research Summary}} 
My current research focus is on high-dimensional data visualization and multivariate volume visualization. Regarding the high-dimensional visualization, my work revolving around visualizing the high-dimensional structures through the interaction and manipulation of the low-dimensional embeddings. I have developed a software framework named \href{http://www.sci.utah.edu/~shusenl/documents.html}{\emph{\underline{DataExplorerHD}}} for visualizing high-dimensional data. It has integrated my research works on related topics, including a method for interactively exploring dimension reduction embedding results [3], as well as a subspace analysis based approach for visualizing high-dimension space through dynamic projection [2] and its multivariate volume visualization extension [1]. In the interactive embedding exploration work, the motivation is to better understand the dimensionality reduction results, which is often used as black boxes, and provide guidelines for manipulating the low-dimensional embeddings. In order to achieve this, both global and local distortion quality measures have been introduced to quantify the structural distortions that occur between high-dimensional and low-dimensional data representations. And through the use of a structural skeleton obtained from clustering, the system allows manipulation of data in the visual space, where the distortion measures provide valuable feedbacks to guide the exploration. In the subspace analysis based high-dimension data visualization work, multiple informative views are discovered through subspace analysis, and then these views are connected through continuous linear projections which create smooth animated transitions among them. These transitions not only help user understand the connection and relationship between different views, more importantly, they also reveal hidden structures that are not obvious or visible by examining individual ``view-point'' alone.

In addition, I am also working on developing multivariate volume visualization method [1][4], especially methods that are in connection with high-dimensional visualization techniques. These two areas are often studied under different contexts: multivariate volume visualization is normally considered as scientific visualization research while high-dimensional data visualization is mostly studied from the perspective of information visualization and visual analytics. They focus on different kinds of data and try to accomplish distinct goals. Despite the differences, recent advances in both areas have shown that they share a number of fundamental techniques and principles. Standard high-dimensional data visualization techniques, such as parallel coordinate, scatterplot and dimensionality reduction have found their way into multivariate volume visualization; understand and capture the structure of the attribute space is essential for both multivariate and high-dimensional dataset. Therefore, useful and interesting techniques may appear through sharing ideas and discover new connections between these two fields. In one of my recent work [1], the subspace analysis based high-dimensional data visualization technique [2] has been adopted as the core component for designing the multivariate transfer function on a ``dynamic canvas''. Instead of restricting the user to a single static view the proposed method allowing user to design the multivariate transfer functions in multiple informative views of the parameter spaces that connect by animated transitions.
 

\textbf{Reference:}\\
\begin{thebibliography}{9}

\bibitem{MultiVariate2014}
{\underline{Shusen Liu}, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci},
\newblock {\bf Multivariate Volume Visualization through Dynamic Projections},
\newblock {\it IEEE Symposium on Large Data Analysis and Visualization}, 2014.

\bibitem{Tech2014}
{\underline{Shusen Liu}, Bei Wang, Jayaraman J. Thiagarajan, Peer-Timo Bremer, Valerio Pascucci},
\newblock {\bf Visual Exploration of High-Dimensional Data through Subspace Analysis and Dynamic Projections},
\newblock {\it  Computer Graphics Forum}, 2015.

\bibitem{Distortion2014}
{\underline{Shusen Liu}, Bei Wang, Peer-Timo Bremer, Valerio Pascucci},
\newblock {\bf Distortion-Guided Structure-Driven Interactive Data Exploration.},
\newblock {\it Computer Graphics Forum}, 33 (3), 101-110, 2014.

\bibitem{GMM2012}
{\underline{Shusen Liu}, Joshua A. Levine, Peer-Timo Bremer, Valerio Pascucci},
\newblock {\bf Gaussian Mixture Model Based Volume Rendering.},
\newblock {\it IEEE Symposium on Large-Scale Data Analysis and Visualization}, 2012. (Best Paper Award)

\end{thebibliography}

\end{resume}
\end{document}
